Enabling Low-Cost Secure Computing on Untrusted In-Memory Architectures

TL;DR

This paper proposes a secure processing-in-memory system that uses multi-party computation techniques to enable low-cost, confidential, and high-performance data processing on untrusted in-memory architectures, addressing the memory wall challenge.

Contribution

It introduces a novel approach combining MPC with PIM to securely outsource computation, leveraging precomputation to reduce bottlenecks, and demonstrates significant speedups on real workloads.

Findings

Achieves up to 14.66x speedup over secure CPU configurations.

Maintains data confidentiality and integrity during outsourced computation.

Effective for applications like deep learning inference and logistic regression.

Abstract

Modern computing systems are limited in performance by the memory bandwidth available to processors, a problem known as the memory wall. Processing-in-Memory (PIM) promises to substantially improve this problem by moving processing closer to the data, improving effective data bandwidth, and leading to superior performance on memory-intensive workloads. However, integrating PIM modules within a secure computing system raises an interesting challenge: unencrypted data has to move off-chip to the PIM, exposing the data to attackers and breaking assumptions on Trusted Computing Bases (TCBs). To tackle this challenge, this paper leverages multi-party computation (MPC) techniques, specifically arithmetic secret sharing and Yao's garbled circuits, to outsource bandwidth-intensive computation securely to PIM. Additionally, we leverage precomputation optimization to prevent the CPU's portion of the MPC from becoming a bottleneck. We evaluate our approach using the UPMEM PIM system over various applications such as Deep Learning Recommendation Model inference and Logistic Regression. Our evaluations demonstrate up to a $14.66\times$ speedup compared to a secure CPU configuration while maintaining data confidentiality and integrity when outsourcing linear and/or nonlinear computation.